It has been shown in recent economic and statistical studies that combining forecasts may produce more accurate forecasts than individual ones. However, the literature on combining forecasts has almost exclusively foc...It has been shown in recent economic and statistical studies that combining forecasts may produce more accurate forecasts than individual ones. However, the literature on combining forecasts has almost exclusively focused on linear combining forecasts. In this paper, a new nonlinear combination forecasting method based on fuzzy inference system is present to overcome the difficulties and drawbacks in linear combination modeling of non-stationary time series. Furthermore, the optimization algorithm based on a hierarchical structure of learning automata is used to identify the parameters of the fuzzy system. Experiment results related to numerical examples demonstrate that the new technique has excellent identification performances and forecasting accuracy superior to other existing linear combining forecasts.展开更多
Pipeline of oil and gas have an increased risk because of pipeline punctures and rupture caused by corrosion. Therefore it is very important to have a reliable way for pipeline corrosion prediction. The corrosion dept...Pipeline of oil and gas have an increased risk because of pipeline punctures and rupture caused by corrosion. Therefore it is very important to have a reliable way for pipeline corrosion prediction. The corrosion depth prediction models that based on the support vector machines and chaos were introduced in this paper. A real example was given in this paper. The predicted results showed that the prediction models have a more higher precision. The two corrosion depth prediction models are reasonable in corrosion research, which can supply a scientific basis for pipeline safety management, service life prediction and repair.展开更多
The objective of this work is to formulate and demonstrate the methodology of multi-models for improving the performance of existing advanced control strategies. Multiple models are used to capture the nonlinear proce...The objective of this work is to formulate and demonstrate the methodology of multi-models for improving the performance of existing advanced control strategies. Multiple models are used to capture the nonlinear process dynamics relating to gain and time constant variations. The multi-model strategy was implemented on several controllers such as Smith-Predictor using PI （Proportional-lntegral） and GPC （Generalized Predictive Control）. Computer simulations and experiments were conducted on several nonlinear systems and compared to the original form of these controllers. The enhanced approach was tested on controlling the screw speed of an injection molding machine and temperature of a steel cylinder.展开更多
The paper＇s aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data （i...The paper＇s aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data （i.e., secular trends, cyclical variations, seasonal effects, and stochastic variations）, they believe the best forecasting model is the one which realistically considers the underlying causal factors in a situational relationship and therefore has the best ＂track records＂ in generating data. Paper＇s models can be adjusted for variations in related a time series which processes a great deal of randomness, to improve the accuracy of the financial forecasts. Because of Na＇fve forecasting models are based on an extrapolation of past values for future. These models may be adjusted for seasonal, secular, and cyclical trends in related data. When a data series processes a great deal of randomness, smoothing techniques, such as moving averages and exponential smoothing, may improve the accuracy of the financial forecasts. But neither Na＇fve models nor smoothing techniques are capable of identifying major future changes in the direction of a situational data series. Hereby, nonlinear techniques, like direct and sequential search approaches, overcome those shortcomings can be used. The methodology which we have used is based on inferential analysis. To build the models to identify the major future changes in the direction of a situational data series, a comparative model building is applied. Hereby, the paper suggests using some of the nonlinear techniques, like direct and sequential search approaches, to reduce the technical shortcomings. The final result of the paper is to manipulate, to prepare, and to integrate heuristic non-linear searching methods to serve calculating adjusted factors to produce the best forecast data.展开更多
在工程中,大型设备和精密仪器在运行时,产生的振动通常会对周围环境造成影响;因此,研究抑制振动的隔振系统及控制方法具有重要意义。为了扩大隔振范围以隔振大型物体,设计了具有多个电磁隔振单元的并联电磁隔振系统,并提出了一种融合Q...在工程中,大型设备和精密仪器在运行时,产生的振动通常会对周围环境造成影响;因此,研究抑制振动的隔振系统及控制方法具有重要意义。为了扩大隔振范围以隔振大型物体,设计了具有多个电磁隔振单元的并联电磁隔振系统,并提出了一种融合Q学习的非线性模型预测控制(Nonlinear Model Predictive Control,NMPC)方法实时调控该多隔振单元系统,以提高系统的隔振性能。基于电磁力、线圈电流和电磁铁间距三者的非线性关系建立了并联电磁隔振系统的动力学方程及状态方程,在此基础上设计了NMPC控制器。其中,利用Q学习方法确定了预测范围,从而避免计算量过大或预测模型不准确的问题;同时,Q学习方法能够优化NMPC方法的目标函数中的权重矩阵V和R。仿真和实验结果表明,在所提出的融合Q学习的NMPC方法控制下的多隔振单元并联系统在外界扰动下,振动幅度显著减小,系统平稳性大大提高。展开更多
Y2001-62725-575 0118801异步取样视频信号同步信号处理=Sync signal process-ing for asynchronously sampled video signals[会,英]/Lares,R.& rothermel,A.//2000 IEEE InternationalSymposium on Circuits and Systems,Vol.3.—...Y2001-62725-575 0118801异步取样视频信号同步信号处理=Sync signal process-ing for asynchronously sampled video signals[会,英]/Lares,R.& rothermel,A.//2000 IEEE InternationalSymposium on Circuits and Systems,Vol.3.—575～578(HC)本文描述了异步数字视频信号的数字同步信号处理,与常用锁相环方法比较,同步脉冲滤波的同步激励和线性预测方法的简化匹配滤波器明显改进了图像稳定性。由于线性预测方法可便于实现对输入信号相位跳跃进位加性适应性,合理硬件作为 FIR 结构可实现简化滤波器系数的线性预测和分析计算。展开更多
基金Funded by the Excellent Young Teachers of MOE (350) and Chongqing Education Committee Foundation
文摘It has been shown in recent economic and statistical studies that combining forecasts may produce more accurate forecasts than individual ones. However, the literature on combining forecasts has almost exclusively focused on linear combining forecasts. In this paper, a new nonlinear combination forecasting method based on fuzzy inference system is present to overcome the difficulties and drawbacks in linear combination modeling of non-stationary time series. Furthermore, the optimization algorithm based on a hierarchical structure of learning automata is used to identify the parameters of the fuzzy system. Experiment results related to numerical examples demonstrate that the new technique has excellent identification performances and forecasting accuracy superior to other existing linear combining forecasts.
文摘Pipeline of oil and gas have an increased risk because of pipeline punctures and rupture caused by corrosion. Therefore it is very important to have a reliable way for pipeline corrosion prediction. The corrosion depth prediction models that based on the support vector machines and chaos were introduced in this paper. A real example was given in this paper. The predicted results showed that the prediction models have a more higher precision. The two corrosion depth prediction models are reasonable in corrosion research, which can supply a scientific basis for pipeline safety management, service life prediction and repair.
文摘The objective of this work is to formulate and demonstrate the methodology of multi-models for improving the performance of existing advanced control strategies. Multiple models are used to capture the nonlinear process dynamics relating to gain and time constant variations. The multi-model strategy was implemented on several controllers such as Smith-Predictor using PI （Proportional-lntegral） and GPC （Generalized Predictive Control）. Computer simulations and experiments were conducted on several nonlinear systems and compared to the original form of these controllers. The enhanced approach was tested on controlling the screw speed of an injection molding machine and temperature of a steel cylinder.
文摘The paper＇s aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data （i.e., secular trends, cyclical variations, seasonal effects, and stochastic variations）, they believe the best forecasting model is the one which realistically considers the underlying causal factors in a situational relationship and therefore has the best ＂track records＂ in generating data. Paper＇s models can be adjusted for variations in related a time series which processes a great deal of randomness, to improve the accuracy of the financial forecasts. Because of Na＇fve forecasting models are based on an extrapolation of past values for future. These models may be adjusted for seasonal, secular, and cyclical trends in related data. When a data series processes a great deal of randomness, smoothing techniques, such as moving averages and exponential smoothing, may improve the accuracy of the financial forecasts. But neither Na＇fve models nor smoothing techniques are capable of identifying major future changes in the direction of a situational data series. Hereby, nonlinear techniques, like direct and sequential search approaches, overcome those shortcomings can be used. The methodology which we have used is based on inferential analysis. To build the models to identify the major future changes in the direction of a situational data series, a comparative model building is applied. Hereby, the paper suggests using some of the nonlinear techniques, like direct and sequential search approaches, to reduce the technical shortcomings. The final result of the paper is to manipulate, to prepare, and to integrate heuristic non-linear searching methods to serve calculating adjusted factors to produce the best forecast data.
文摘在工程中,大型设备和精密仪器在运行时,产生的振动通常会对周围环境造成影响;因此,研究抑制振动的隔振系统及控制方法具有重要意义。为了扩大隔振范围以隔振大型物体,设计了具有多个电磁隔振单元的并联电磁隔振系统,并提出了一种融合Q学习的非线性模型预测控制(Nonlinear Model Predictive Control,NMPC)方法实时调控该多隔振单元系统,以提高系统的隔振性能。基于电磁力、线圈电流和电磁铁间距三者的非线性关系建立了并联电磁隔振系统的动力学方程及状态方程,在此基础上设计了NMPC控制器。其中,利用Q学习方法确定了预测范围,从而避免计算量过大或预测模型不准确的问题;同时,Q学习方法能够优化NMPC方法的目标函数中的权重矩阵V和R。仿真和实验结果表明,在所提出的融合Q学习的NMPC方法控制下的多隔振单元并联系统在外界扰动下,振动幅度显著减小,系统平稳性大大提高。
文摘Y2001-62725-575 0118801异步取样视频信号同步信号处理=Sync signal process-ing for asynchronously sampled video signals[会,英]/Lares,R.& rothermel,A.//2000 IEEE InternationalSymposium on Circuits and Systems,Vol.3.—575～578(HC)本文描述了异步数字视频信号的数字同步信号处理,与常用锁相环方法比较,同步脉冲滤波的同步激励和线性预测方法的简化匹配滤波器明显改进了图像稳定性。由于线性预测方法可便于实现对输入信号相位跳跃进位加性适应性,合理硬件作为 FIR 结构可实现简化滤波器系数的线性预测和分析计算。
